Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x112da1f98>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [9]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[9]:
<matplotlib.image.AxesImage at 0x11c662b70>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
/Users/Rossonero/anaconda/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [11]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    inputs = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return inputs, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [13]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        #input layer is 28 x 28 x 3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        #14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        #7x7x128
        
        #x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        #bn3 = tf.layers.batch_normalization(x3, training=True)
        #relu3 = tf.maximum(alpha * bn3, bn3)
        #4x4x256
        
        # Flatten
        flat = tf.reshape(relu2, (-1, 7*7*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [ ]:
aaaa= True
not aaaa
In [31]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha=0.2
    with tf.variable_scope('generator', reuse=not is_train):
        #First fuuly connected layer
        x1 = tf.layers.dense(z, 7*7*512)
        #reshape to start the conv stack
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        #7x7x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 14x14x256 now
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 28x28x128 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')
        # 28x28x3 now
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [24]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
                    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
                    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
                    tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [25]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    
    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [26]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [29]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model

    print(data_shape)
    input_real, input_z, _ = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
        
    d_loss, g_loss = model_loss(input_real, input_z,
                                              data_shape[3])        
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    saver = tf.train.Saver()

    losses = []
    steps = 0
    print_every = 50
    show_every = 50
    print('------start training------')
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images *= 2
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images})
                
                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(steps, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, 64, input_z, data_shape[3], data_image_mode)
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [30]:
batch_size = 32
z_dim = 200
learning_rate = 0.0002
beta1 = 0.3

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
#tf. reset.default_graph()
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
(60000, 28, 28, 1)
------start training------
Epoch 50/2... Discriminator Loss: 2.8471... Generator Loss: 0.0965
Epoch 100/2... Discriminator Loss: 2.0746... Generator Loss: 0.2389
Epoch 150/2... Discriminator Loss: 2.3202... Generator Loss: 0.2282
Epoch 200/2... Discriminator Loss: 1.9989... Generator Loss: 0.3133
Epoch 250/2... Discriminator Loss: 1.7086... Generator Loss: 0.4043
Epoch 300/2... Discriminator Loss: 1.7771... Generator Loss: 0.3002
Epoch 350/2... Discriminator Loss: 1.2494... Generator Loss: 0.6598
Epoch 400/2... Discriminator Loss: 1.9162... Generator Loss: 0.1865
Epoch 450/2... Discriminator Loss: 1.0755... Generator Loss: 1.4722
Epoch 500/2... Discriminator Loss: 1.7757... Generator Loss: 0.2270
Epoch 550/2... Discriminator Loss: 1.2972... Generator Loss: 0.6064
Epoch 600/2... Discriminator Loss: 1.5632... Generator Loss: 0.6140
Epoch 650/2... Discriminator Loss: 1.9293... Generator Loss: 0.1951
Epoch 700/2... Discriminator Loss: 1.4221... Generator Loss: 0.6325
Epoch 750/2... Discriminator Loss: 1.5002... Generator Loss: 0.3816
Epoch 800/2... Discriminator Loss: 2.3817... Generator Loss: 0.1185
Epoch 850/2... Discriminator Loss: 1.2972... Generator Loss: 0.5774
Epoch 900/2... Discriminator Loss: 1.2430... Generator Loss: 0.6460
Epoch 950/2... Discriminator Loss: 1.4274... Generator Loss: 0.7434
Epoch 1000/2... Discriminator Loss: 1.7858... Generator Loss: 0.2283
Epoch 1050/2... Discriminator Loss: 1.4183... Generator Loss: 0.4677
Epoch 1100/2... Discriminator Loss: 1.8257... Generator Loss: 0.3091
Epoch 1150/2... Discriminator Loss: 1.6454... Generator Loss: 0.4813
Epoch 1200/2... Discriminator Loss: 1.6091... Generator Loss: 0.7354
Epoch 1250/2... Discriminator Loss: 1.3907... Generator Loss: 0.5866
Epoch 1300/2... Discriminator Loss: 1.5824... Generator Loss: 0.3895
Epoch 1350/2... Discriminator Loss: 1.5211... Generator Loss: 0.4390
Epoch 1400/2... Discriminator Loss: 1.7183... Generator Loss: 0.4705
Epoch 1450/2... Discriminator Loss: 1.3169... Generator Loss: 0.5243
Epoch 1500/2... Discriminator Loss: 1.1706... Generator Loss: 0.9710
Epoch 1550/2... Discriminator Loss: 1.3827... Generator Loss: 0.7676
Epoch 1600/2... Discriminator Loss: 1.4179... Generator Loss: 0.6195
Epoch 1650/2... Discriminator Loss: 1.6065... Generator Loss: 0.3909
Epoch 1700/2... Discriminator Loss: 1.3506... Generator Loss: 0.5615
Epoch 1750/2... Discriminator Loss: 1.1802... Generator Loss: 0.6163
Epoch 1800/2... Discriminator Loss: 1.2927... Generator Loss: 0.5334
Epoch 1850/2... Discriminator Loss: 1.5036... Generator Loss: 0.3903
Epoch 1900/2... Discriminator Loss: 1.5630... Generator Loss: 0.3528
Epoch 1950/2... Discriminator Loss: 1.2715... Generator Loss: 1.0220
Epoch 2000/2... Discriminator Loss: 1.2368... Generator Loss: 1.0422
Epoch 2050/2... Discriminator Loss: 1.2237... Generator Loss: 0.6632
Epoch 2100/2... Discriminator Loss: 1.2800... Generator Loss: 0.8689
Epoch 2150/2... Discriminator Loss: 1.8009... Generator Loss: 0.2989
Epoch 2200/2... Discriminator Loss: 1.5451... Generator Loss: 0.5525
Epoch 2250/2... Discriminator Loss: 1.4861... Generator Loss: 0.3881
Epoch 2300/2... Discriminator Loss: 1.3704... Generator Loss: 0.4682
Epoch 2350/2... Discriminator Loss: 1.0196... Generator Loss: 0.7123
Epoch 2400/2... Discriminator Loss: 1.1228... Generator Loss: 1.0178
Epoch 2450/2... Discriminator Loss: 1.3060... Generator Loss: 0.6623
Epoch 2500/2... Discriminator Loss: 1.6336... Generator Loss: 0.3396
Epoch 2550/2... Discriminator Loss: 1.2042... Generator Loss: 0.5976
Epoch 2600/2... Discriminator Loss: 1.3826... Generator Loss: 0.3962
Epoch 2650/2... Discriminator Loss: 1.1939... Generator Loss: 0.9019
Epoch 2700/2... Discriminator Loss: 1.4900... Generator Loss: 0.4148
Epoch 2750/2... Discriminator Loss: 1.2343... Generator Loss: 0.5121
Epoch 2800/2... Discriminator Loss: 1.2899... Generator Loss: 0.4766
Epoch 2850/2... Discriminator Loss: 1.3675... Generator Loss: 0.4899
Epoch 2900/2... Discriminator Loss: 1.3764... Generator Loss: 0.9464
Epoch 2950/2... Discriminator Loss: 1.3139... Generator Loss: 0.4238
Epoch 3000/2... Discriminator Loss: 1.2713... Generator Loss: 0.5319
Epoch 3050/2... Discriminator Loss: 1.3368... Generator Loss: 0.9941
Epoch 3100/2... Discriminator Loss: 1.2839... Generator Loss: 0.6939
Epoch 3150/2... Discriminator Loss: 1.6047... Generator Loss: 1.3724
Epoch 3200/2... Discriminator Loss: 1.2532... Generator Loss: 0.4961
Epoch 3250/2... Discriminator Loss: 1.3387... Generator Loss: 0.4301
Epoch 3300/2... Discriminator Loss: 1.6131... Generator Loss: 0.2730
Epoch 3350/2... Discriminator Loss: 1.3179... Generator Loss: 0.9046
Epoch 3400/2... Discriminator Loss: 1.5898... Generator Loss: 0.3159
Epoch 3450/2... Discriminator Loss: 1.8480... Generator Loss: 1.9665
Epoch 3500/2... Discriminator Loss: 1.3244... Generator Loss: 0.5183
Epoch 3550/2... Discriminator Loss: 1.3150... Generator Loss: 0.4415
Epoch 3600/2... Discriminator Loss: 1.8191... Generator Loss: 0.3692
Epoch 3650/2... Discriminator Loss: 1.0252... Generator Loss: 0.7062
Epoch 3700/2... Discriminator Loss: 1.3702... Generator Loss: 1.3041
Epoch 3750/2... Discriminator Loss: 1.0890... Generator Loss: 0.8748

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [32]:
batch_size = 32
z_dim = 200
learning_rate = 0.0002
beta1 = 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
(202599, 28, 28, 3)
------start training------
Epoch 50/1... Discriminator Loss: 3.7942... Generator Loss: 0.0577
Epoch 100/1... Discriminator Loss: 2.8667... Generator Loss: 0.0936
Epoch 150/1... Discriminator Loss: 3.0712... Generator Loss: 0.0630
Epoch 200/1... Discriminator Loss: 1.1243... Generator Loss: 3.2965
Epoch 250/1... Discriminator Loss: 1.0036... Generator Loss: 2.6184
Epoch 300/1... Discriminator Loss: 0.5811... Generator Loss: 1.3548
Epoch 350/1... Discriminator Loss: 0.5424... Generator Loss: 2.4825
Epoch 400/1... Discriminator Loss: 0.6018... Generator Loss: 1.3682
Epoch 450/1... Discriminator Loss: 0.9580... Generator Loss: 4.8050
Epoch 500/1... Discriminator Loss: 2.3739... Generator Loss: 0.1274
Epoch 550/1... Discriminator Loss: 0.9770... Generator Loss: 0.7622
Epoch 600/1... Discriminator Loss: 0.3887... Generator Loss: 2.3318
Epoch 650/1... Discriminator Loss: 0.3287... Generator Loss: 4.9040
Epoch 700/1... Discriminator Loss: 0.8171... Generator Loss: 2.4552
Epoch 750/1... Discriminator Loss: 0.8373... Generator Loss: 0.8697
Epoch 800/1... Discriminator Loss: 0.4022... Generator Loss: 2.4299
Epoch 850/1... Discriminator Loss: 1.5329... Generator Loss: 0.4864
Epoch 900/1... Discriminator Loss: 1.2273... Generator Loss: 0.5880
Epoch 950/1... Discriminator Loss: 1.7538... Generator Loss: 0.5161
Epoch 1000/1... Discriminator Loss: 1.4319... Generator Loss: 0.5586
Epoch 1050/1... Discriminator Loss: 1.2348... Generator Loss: 1.3477
Epoch 1100/1... Discriminator Loss: 1.0302... Generator Loss: 0.7047
Epoch 1150/1... Discriminator Loss: 2.1903... Generator Loss: 0.1819
Epoch 1200/1... Discriminator Loss: 1.5374... Generator Loss: 0.6585
Epoch 1250/1... Discriminator Loss: 1.9588... Generator Loss: 0.1971
Epoch 1300/1... Discriminator Loss: 0.6414... Generator Loss: 1.6568
Epoch 1350/1... Discriminator Loss: 1.0316... Generator Loss: 1.1047
Epoch 1400/1... Discriminator Loss: 1.6298... Generator Loss: 0.3665
Epoch 1450/1... Discriminator Loss: 1.0868... Generator Loss: 1.5693
Epoch 1500/1... Discriminator Loss: 1.1349... Generator Loss: 1.5300
Epoch 1550/1... Discriminator Loss: 1.9227... Generator Loss: 0.2158
Epoch 1600/1... Discriminator Loss: 1.2273... Generator Loss: 0.5848
Epoch 1650/1... Discriminator Loss: 2.2292... Generator Loss: 1.2346
Epoch 1700/1... Discriminator Loss: 1.2314... Generator Loss: 1.0145
Epoch 1750/1... Discriminator Loss: 0.8515... Generator Loss: 1.0276
Epoch 1800/1... Discriminator Loss: 1.0845... Generator Loss: 0.8880
Epoch 1850/1... Discriminator Loss: 1.2482... Generator Loss: 0.6384
Epoch 1900/1... Discriminator Loss: 1.8673... Generator Loss: 0.2568
Epoch 1950/1... Discriminator Loss: 2.1572... Generator Loss: 1.0095
Epoch 2000/1... Discriminator Loss: 1.0946... Generator Loss: 0.6731
Epoch 2050/1... Discriminator Loss: 1.3170... Generator Loss: 0.4323
Epoch 2100/1... Discriminator Loss: 1.2547... Generator Loss: 0.6366
Epoch 2150/1... Discriminator Loss: 2.0264... Generator Loss: 0.2058
Epoch 2200/1... Discriminator Loss: 1.3972... Generator Loss: 0.4596
Epoch 2250/1... Discriminator Loss: 2.4157... Generator Loss: 0.9584
Epoch 2300/1... Discriminator Loss: 1.9182... Generator Loss: 0.2699
Epoch 2350/1... Discriminator Loss: 1.5396... Generator Loss: 0.4250
Epoch 2400/1... Discriminator Loss: 1.6714... Generator Loss: 0.3810
Epoch 2450/1... Discriminator Loss: 1.4895... Generator Loss: 0.4779
Epoch 2500/1... Discriminator Loss: 1.1929... Generator Loss: 0.9587
Epoch 2550/1... Discriminator Loss: 0.6906... Generator Loss: 1.5353
Epoch 2600/1... Discriminator Loss: 2.2106... Generator Loss: 0.1504
Epoch 2650/1... Discriminator Loss: 0.8592... Generator Loss: 0.8400
Epoch 2700/1... Discriminator Loss: 1.4994... Generator Loss: 0.4116
Epoch 2750/1... Discriminator Loss: 1.3364... Generator Loss: 0.5221
Epoch 2800/1... Discriminator Loss: 1.2308... Generator Loss: 0.5401
Epoch 2850/1... Discriminator Loss: 0.6099... Generator Loss: 1.4329
Epoch 2900/1... Discriminator Loss: 1.6786... Generator Loss: 0.6440
Epoch 2950/1... Discriminator Loss: 1.2673... Generator Loss: 0.7889
Epoch 3000/1... Discriminator Loss: 1.4613... Generator Loss: 0.7084
Epoch 3050/1... Discriminator Loss: 0.9258... Generator Loss: 0.9057
Epoch 3100/1... Discriminator Loss: 1.6867... Generator Loss: 0.4277
Epoch 3150/1... Discriminator Loss: 1.5092... Generator Loss: 0.4316
Epoch 3200/1... Discriminator Loss: 1.3707... Generator Loss: 0.5847
Epoch 3250/1... Discriminator Loss: 1.5491... Generator Loss: 0.4864
Epoch 3300/1... Discriminator Loss: 1.1349... Generator Loss: 0.9102
Epoch 3350/1... Discriminator Loss: 1.6783... Generator Loss: 0.3097
Epoch 3400/1... Discriminator Loss: 0.9579... Generator Loss: 0.7511
Epoch 3450/1... Discriminator Loss: 2.0816... Generator Loss: 0.2049
Epoch 3500/1... Discriminator Loss: 1.4376... Generator Loss: 0.5632
Epoch 3550/1... Discriminator Loss: 1.4398... Generator Loss: 0.5299
Epoch 3600/1... Discriminator Loss: 1.0940... Generator Loss: 1.1160
Epoch 3650/1... Discriminator Loss: 1.3509... Generator Loss: 0.5463
Epoch 3700/1... Discriminator Loss: 1.4823... Generator Loss: 0.5677
Epoch 3750/1... Discriminator Loss: 1.1773... Generator Loss: 0.8733
Epoch 3800/1... Discriminator Loss: 0.9313... Generator Loss: 1.6949
Epoch 3850/1... Discriminator Loss: 0.8419... Generator Loss: 1.3902
Epoch 3900/1... Discriminator Loss: 0.7259... Generator Loss: 1.3962
Epoch 3950/1... Discriminator Loss: 1.5254... Generator Loss: 0.4885
Epoch 4000/1... Discriminator Loss: 1.0980... Generator Loss: 0.7670
Epoch 4050/1... Discriminator Loss: 1.2977... Generator Loss: 1.0318
Epoch 4100/1... Discriminator Loss: 1.3757... Generator Loss: 0.6557
Epoch 4150/1... Discriminator Loss: 1.7393... Generator Loss: 0.4180
Epoch 4200/1... Discriminator Loss: 1.9880... Generator Loss: 0.2181
Epoch 4250/1... Discriminator Loss: 1.2399... Generator Loss: 0.5407
Epoch 4300/1... Discriminator Loss: 1.4621... Generator Loss: 0.4391
Epoch 4350/1... Discriminator Loss: 1.7580... Generator Loss: 0.4103
Epoch 4400/1... Discriminator Loss: 0.7822... Generator Loss: 1.5440
Epoch 4450/1... Discriminator Loss: 1.8663... Generator Loss: 0.2597
Epoch 4500/1... Discriminator Loss: 1.7730... Generator Loss: 0.2822
Epoch 4550/1... Discriminator Loss: 1.5507... Generator Loss: 0.3608
Epoch 4600/1... Discriminator Loss: 1.1836... Generator Loss: 0.5765
Epoch 4650/1... Discriminator Loss: 1.3877... Generator Loss: 0.6167
Epoch 4700/1... Discriminator Loss: 1.5718... Generator Loss: 0.3994
Epoch 4750/1... Discriminator Loss: 0.7042... Generator Loss: 2.2843
Epoch 4800/1... Discriminator Loss: 0.6113... Generator Loss: 3.5394
Epoch 4850/1... Discriminator Loss: 1.5754... Generator Loss: 0.7254
Epoch 4900/1... Discriminator Loss: 1.3551... Generator Loss: 0.4437
Epoch 4950/1... Discriminator Loss: 1.5413... Generator Loss: 0.4363
Epoch 5000/1... Discriminator Loss: 1.4033... Generator Loss: 0.5435
Epoch 5050/1... Discriminator Loss: 1.5858... Generator Loss: 0.3334
Epoch 5100/1... Discriminator Loss: 1.3614... Generator Loss: 0.6891
Epoch 5150/1... Discriminator Loss: 1.5855... Generator Loss: 0.4511
Epoch 5200/1... Discriminator Loss: 1.5642... Generator Loss: 0.8988
Epoch 5250/1... Discriminator Loss: 1.4957... Generator Loss: 0.5148
Epoch 5300/1... Discriminator Loss: 1.5934... Generator Loss: 0.3755
Epoch 5350/1... Discriminator Loss: 1.3986... Generator Loss: 0.4736
Epoch 5400/1... Discriminator Loss: 1.5514... Generator Loss: 0.5051
Epoch 5450/1... Discriminator Loss: 1.5831... Generator Loss: 0.5048
Epoch 5500/1... Discriminator Loss: 1.3760... Generator Loss: 0.6036
Epoch 5550/1... Discriminator Loss: 1.6502... Generator Loss: 0.4379
Epoch 5600/1... Discriminator Loss: 1.5743... Generator Loss: 0.5185
Epoch 5650/1... Discriminator Loss: 1.2661... Generator Loss: 0.6027
Epoch 5700/1... Discriminator Loss: 1.3889... Generator Loss: 0.5920
Epoch 5750/1... Discriminator Loss: 1.5158... Generator Loss: 0.5331
Epoch 5800/1... Discriminator Loss: 1.4467... Generator Loss: 0.4654
Epoch 5850/1... Discriminator Loss: 1.2323... Generator Loss: 0.7329
Epoch 5900/1... Discriminator Loss: 1.4936... Generator Loss: 0.4151
Epoch 5950/1... Discriminator Loss: 1.3420... Generator Loss: 0.6428
Epoch 6000/1... Discriminator Loss: 1.6719... Generator Loss: 0.3967
Epoch 6050/1... Discriminator Loss: 1.5087... Generator Loss: 0.4966
Epoch 6100/1... Discriminator Loss: 1.5244... Generator Loss: 0.4891
Epoch 6150/1... Discriminator Loss: 1.7160... Generator Loss: 0.6861
Epoch 6200/1... Discriminator Loss: 1.1945... Generator Loss: 0.8130
Epoch 6250/1... Discriminator Loss: 1.5519... Generator Loss: 0.6331
Epoch 6300/1... Discriminator Loss: 1.6880... Generator Loss: 0.3749

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

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